Is this skill compatible with Next.js?

Absolutely, it features patterns specifically designed for Next.js App Router, including Server Actions and client-side hooks.

How does it handle vector databases?

It includes standardized patterns for generating embeddings and implementing Retrieval-Augmented Generation (RAG) workflows.

Does it support structured data output?

Yes, it includes patterns for using Zod schemas with generateObject to ensure AI responses follow a specific JSON structure.

What LLM providers does this skill support?

It supports major providers including OpenAI, Anthropic (Claude), and Google (Gemini) through the Vercel AI SDK's unified interface.

Can I use this for real-time streaming?

Yes, the skill provides patterns for streaming text responses and tool results using both Route Handlers and Server Actions.

Vercel AI SDK Integrator

Name: Vercel AI SDK Integrator
Author: Naw3

byNaw3

Data Science & ML

Implements modern AI patterns including streaming responses, tool calling, and structured outputs using the Vercel AI SDK.

About

This skill provides comprehensive patterns and best practices for building sophisticated AI-powered applications with the Vercel AI SDK. It enables developers to seamlessly integrate multi-provider LLM support, handle streaming data in both server and client environments, and implement advanced features like tool calling, RAG (Retrieval-Augmented Generation), and type-safe structured outputs. Whether building a simple chat interface or a complex agentic system, this skill offers the implementation logic and architectural guidance needed for production-grade AI integration.

Key Features

RAG patterns and embedding generation for vector search
Multi-provider support for OpenAI, Anthropic, and Google Gemini
Type-safe tool calling and function execution implementation
Streaming LLM response patterns for React and Route Handlers
Structured data generation using Zod and generateObject
0 GitHub stars

Use Cases

Developing AI agents that can interact with external APIs through tools
Building real-time AI chat interfaces with streaming responses
Creating data extraction pipelines that output valid JSON schemas

About

Key Features

RAG patterns and embedding generation for vector search
Multi-provider support for OpenAI, Anthropic, and Google Gemini
Type-safe tool calling and function execution implementation
Streaming LLM response patterns for React and Route Handlers
Structured data generation using Zod and generateObject
0 GitHub stars

Use Cases

Developing AI agents that can interact with external APIs through tools
Building real-time AI chat interfaces with streaming responses
Creating data extraction pipelines that output valid JSON schemas